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Structure Learning in Nested Effects Models

Author

Listed:
  • Tresch Achim

    (Johannes Gutenberg University Mainz)

  • Markowetz Florian

    (Princeton University)

Abstract

Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g., the effects showing in gene expression profiles or as morphological features of the perturbed cell.In this paper we expand the statistical basis of NEMs in four directions. First, we derive a new formula for the likelihood function of a NEM, which generalizes previous results for binary data. Second, we prove model identifiability under mild assumptions. Third, we show that the new formulation of the likelihood allows efficiency in traversing model space. Fourth, we incorporate prior knowledge and an automated variable selection criterion to decrease the influence of noise in the data.

Suggested Citation

  • Tresch Achim & Markowetz Florian, 2008. "Structure Learning in Nested Effects Models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-28, March.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:1:n:9
    DOI: 10.2202/1544-6115.1332
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    References listed on IDEAS

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    1. Andrew Fire & SiQun Xu & Mary K. Montgomery & Steven A. Kostas & Samuel E. Driver & Craig C. Mello, 1998. "Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans," Nature, Nature, vol. 391(6669), pages 806-811, February.
    2. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
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    Cited by:

    1. Theresa Niederberger & Stefanie Etzold & Michael Lidschreiber & Kerstin C Maier & Dietmar E Martin & Holger Fröhlich & Patrick Cramer & Achim Tresch, 2012. "MC EMiNEM Maps the Interaction Landscape of the Mediator," PLOS Computational Biology, Public Library of Science, vol. 8(6), pages 1-10, June.
    2. Ali Shojaie & Alexandra Jauhiainen & Michael Kallitsis & George Michailidis, 2014. "Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-16, February.

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